Cognitive biases and greenwashing in corporate sustainability: Biased to deceive.

CHAP: Channel-spatial hierarchical adversarial perturbation for semi-supervised medical image segmentation.

Mobile device-enabled interventions known as digital therapeutics (DTx) are increasingly used to prevent chronic disease by targeting psychological and behavioral processes. Individuals' unique experiences while receiving DTx comprise real-world evidence (RWE) for evaluating DTx performance. An emerging strategy for early-stage DTx formative work uses small sample sizes, which facilitate efficient iteration and agile learning, while evaluating performance against descriptive benchmarks defined a priori, therefore minimizing the risk for confirmation bias. This study test benchmarks from the DTx RWE framework to formatively evaluate a novel DTx (the eMOTION study) to enhance affective response (ie, how people feel) during physical activity (PA). This study aimed to determine whether the eMOTION DTx met a priori benchmarks for safety (<1% of participants report an adverse event), plausibility (≥51% of participants experience increased enjoyment in PA), usability (eg, ≥51% of participants report adequate usability), sustainability, feasibility (eg, <70% of participants report dissatisfaction), and equity (equity and accessibility are approximately equal across subgroups). Participants (N=36; mean age 46, SD 14 years; 20/37, 54% female) underwent stratified random assignment to test one of four DTx versions for 14 days (n=9 each): (1) intensity PA goals, (2) affect PA goals with type and context recommendations, (3) affect PA goals with savoring exercises, and (4) affect PA goals with type, context, and savoring. Participants completed daily intervention sessions, asking them to focus on achieving a target heart rate (intensity) or feeling good (affect) during PA. Smartwatches were used to track PA and answer ecological momentary assessment (EMA) questions about how they felt during PA. Performance toward benchmarks was primarily assessed via official Institutional Review Board reporting channels (safety), interviews (plausibility, accessibility, and usability), and questionnaires (System Usability Scale [usability], Delighted-Terrible Scale [sustainability and feasibility], and equity). The eMOTION DTx versions exceeded all a priori safety, plausibility, accessibility, usability, sustainability, feasibility, and equity thresholds. For safety, no adverse events were reported. Regarding plausibility, more than half of the participants who received affect goals reported increased PA enjoyment at the end of the study. Moreover, 64%-72% (23-26 out of 36) of participants rated the DTx at or above the standard System Usability Scale cutoff point for acceptable usability. More than 60% (22/36) of participants reported satisfaction with all DTx components, supporting DTx sustainability and feasibility. Finally, there was evidence for equity, with plausibility and accessibility comparable across sex, race, ethnicity, income, age, BMI, mobility, and physical constraint subgroups. Since DTx RWE Framework benchmarks for safety, plausibility, accessibility, usability, sustainability, feasibility, and equity were largely met, the eMOTION Study DTx is ready for a full-scale efficacy trial to refine the DTx and optimize efficiency and feasibility. Our approach incorporated transparent decision-making to generate results that are more readily translatable, easily replicable, and reflect current best practices in the field of DTx. ClinicalTrials.gov NCT06125964; https://clinicaltrials.gov/study/NCT06125964.

The scientists who alerted us to the dangers of radiation by Ian Fairlie and Cindy Folkers.

This study examines the decision-making process of the Greater Middle East Project (GMEP) from the perspective of cognitive biases and behavioral decision-making psychology within a normative-analytical comparative framework. The primary objective is to explain which cognitive mechanisms shaped the decision logic of GMEP and why it systematically deviated from normative rationality assumptions. The study adopts a qualitative research approach and single-case analysis method, employing conceptual analysis, comparative theoretical evaluation, and document review based on secondary sources. Research findings reveal that confirmation bias, overconfidence bias, framing effect, and planning fallacy exhibited realization levels ranging from approximately seventy to eighty percent in the decision-making process. These biases were found to operate not independently but within a cyclical interaction network that mutually reinforces one another. The study demonstrates that cognitive biases are reinforced not only at the individual level but also within institutional decisionmaking structures, with groupthink dynamics and epistemic communities playing mediating roles in this reinforcement process. The profound gap between normative discourse and analytical outcomes is conceptualized as the product of cognitive distortions. The research makes an original contribution to the behavioral international relations literature by demonstrating that foreign policy failures can be explained through structural cognitive dynamics rather than individual shortcomings. The theoretical contribution lies in integrating normative and behavioral approaches within a comparative framework, while the practical contribution consists of developing concrete recommendations for integrating cognitive correction mechanisms into policy design.

Expectations can shape perception and potentially lead to self-fulfilling prophecies such as placebo effects that persist or grow over time. Nonetheless, whether and how unreinforced and unconditioned social cues (i.e., suggestions about future experiences that have not been reinforced with reward or punishment) can create and sustain such effects is unknown. We conducted a set of experiments in which participants (N = 111) experienced stimuli eliciting somatic pain (heat), vicarious pain (videos of others in pain), and cognitive effort (a mental-rotation task), at three intensity levels each. Before each stimulus, participants viewed a social cue that ostensibly indicated ratings from 10 other participants but was in fact randomized to a high or low mean aversiveness level independent of actual stimulus intensity. Across all tasks, participants' expectations and experience ratings shifted in line with the cues, with high-aversive cues leading to higher perceived aversiveness. Computational modeling and behavioral analysis revealed lower learning rates for prediction errors inconsistent with the trial's cue value (e.g., better than expected for high-aversive cues) and higher learning rates for prediction errors consistent with the cue value (e.g., worse than expected for high-aversive cues). These findings reveal a confirmation bias in learning: people update more when outcomes align with expectations. Combined with expectation effects on perception, this bias helps sustain social cue effects. Together, these mechanisms show how social information can shape perception and learning, giving rise to self-fulfilling prophecies.

This paper systematically explores the manifestations of cognitive biases in group decision-making processes and their impact on decision quality. As a decision-making form characterized by multi-agent participation and collective deliberation, group decision-making is widely applied in organizational management, public policy formulation, and social governance. However, cognitive biases inherent in individual members and their interactions significantly constrain decision effectiveness. The article begins by reviewing the basic processes and characteristics of group decision-making, as well as the psychological and social origins of cognitive biases. Building on this foundation, it focuses on analyzing the specific manifestations and mechanisms of typical cognitive biases—such as availability bias, selective attention, representativeness bias, anchoring effects, confirmation bias, and groupthink—during the three stages of information collection, information evaluation, and decision formation. These biases lead to fragmented information acquisition, distorted judgment, and opinion convergence, thereby reducing decision accuracy, increasing risks, and hindering team collaboration. To address these issues, the article proposes systematic strategies from three aspects: enhancing members’ cognitive abilities, optimizing decision-making processes and mechanisms, and fostering an open and inclusive team atmosphere.

Predicting helpfulness of multimodal reviews with customer confirmation bias: A hierarchically trusted multi-view deep learning method

While several theoretical models of psychopathy have been offered over time and across academic disciplines, the most popular today is Hare’s (1980, 1991, 2003) Psychopathy Checklist. It provides a list of behaviors commonly seen in psychopaths, clustered into four groups of traits: antisocial (e.g., juvenile delinquency, poor behavioral control), lifestyle (e.g., impulsiveness, lack of realistic goals), interpersonal (e.g., grandiose self-worth, conning manipulation), and affective (emotional issues, e.g., lack of remorse and empathy for others). We used the checklist as a guide to analyze whether Jim Jones, leader of Peoples Temple, would qualify as a psychopath under this model. Our comparison included both archival evidence from memoirs and biographies and a brief email interview with Jones’s surviving biological son, Stephan Jones. We conclude that Jim Jones does qualify as a psychopath according to the Hare model. While this label is cautious given concerns such as hindsight and confirmation bias, the model may be useful for analysis of leaders of other, more contemporary, coercive groups or cults.

In the digital era, religious authority has undergone a significant shift from physical pulpits in mosques to "algorithmic pulpits" on social media. This study aims to analyze how content personalization mechanisms and filter bubbles affect perceptions of religious moderation among users. Social media algorithms are designed to maximize engagement by presenting content tailored to user preferences, inadvertently creating echo chambers. Using a qualitative method with a literature review and critical discourse analysis approach, this research explores the psychological and sociological impacts of this automated content curation. The results indicate that algorithms tend to reinforce confirmation bias, where users are exposed only to religious narratives that validate their pre-existing views, while isolating them from divergent perspectives (the religious other). This potential erodes the values of religious moderation (wasathiyah), which emphasizes balance and tolerance, and fosters polarization and extremism based on decontextualized texts. The study concludes that algorithmic literacy is essential as part of religious literacy, preventing believers from being trapped in blind fanaticism constructed by machines.

ABSTRACT The increasing use of generative artificial intelligence (AI) in student assessment has led to institutional reliance on detection tools. Unlike plagiarism detection, AI detection relies on unverifiable probabilistic estimates. In this paper, we argue that generative AI detection should not be used in education due to its methodological imperfections, violation of procedural fairness, and unverifiable outputs. Generative AI detectors cannot be tested in real-world conditions where the true origin of a text is unknown. Attempts to validate results through linguistic markers, multiple tools, or comparisons with past work introduce confirmation bias rather than independent verification. Moreover, categorising text as human- or AI-generated imposes a false dichotomy that ignores work created with, not by, AI. Generative AI detection also raises security concerns. Academic integrity investigations must rely on evidence meeting the balance of probabilities standard, which generative AI detection scores do not satisfy.

In the digital era, religious authority has undergone a significant shift from physical pulpits in mosques to "algorithmic pulpits" on social media. This study aims to analyze how content personalization mechanisms and filter bubbles affect perceptions of religious moderation among users. Social media algorithms are designed to maximize engagement by presenting content tailored to user preferences, inadvertently creating echo chambers. Using a qualitative method with a literature review and critical discourse analysis approach, this research explores the psychological and sociological impacts of this automated content curation. The results indicate that algorithms tend to reinforce confirmation bias, where users are exposed only to religious narratives that validate their pre-existing views, while isolating them from divergent perspectives (the religious other). This potential erodes the values of religious moderation (wasathiyah), which emphasizes balance and tolerance, and fosters polarization and extremism based on decontextualized texts. The study concludes that algorithmic literacy is essential as part of religious literacy, preventing believers from being trapped in blind fanaticism constructed by machines.

Empirical research in economics and finance rarely speaks with a single, unambiguous voice. The results we obtain often depend on a series of methodological choices—how the model is specified, which time period or countries are included, how key variables are measured, and which estimation techniques are applied. These choices can make it possible, sometimes inadvertently, to produce results that align with prior beliefs. This paper uses three versions of the Kuznets curve—the original income–inequality relationship, the environmental Kuznets curve, and the financial Kuznets curve—to illustrate this problem. In each case, the underlying hypothesis is represented by a simple quadratic relationship with income per capita, while the dependent variable varies according to the dimension under study. Both cross-sectional and time-series data are employed to explore these relationships. The empirical results show a striking degree of sensitivity. Small changes in measurement, sample selection, or estimation method often leads to different—and occasionally contradictory—conclusions. This sensitivity creates room for selective reporting and confirmation bias, particularly when the issues under investigation are ideologically charged. Rather than offering definitive answers, the findings highlight the limits of point estimates in such settings. The paper therefore points toward alternative approaches that confront model uncertainty more directly, emphasizing the reporting of entire distributions of estimated effects instead of relying on single, seemingly precise coefficients.

ABSTRACT A critical analysis is conducted on the indicators of falsification of incisions on Roman‐era material, as presented by the Institute of Cultural Heritage of Spain (ICHS) regarding the ostraca from the archaeological site of Iruña‐Veleia Spain. The phases prior to laboratory analysis, the analytical phase, and the results phase presented by ICHS are discussed. Several issues are identified, including confirmation bias, selection bias, and problems of undercoverage. Additionally, relevant previous contributions are omitted, and serious deficiencies in the preservation of the evidence are observed. There is also a lack of analysis regarding the probability of occurrence of the proposed anomalies, as well as an absence of mention of measurement biases related to the tools used. The lack of reproducibility of the analyses and conclusions based on subjective testimonies compromises the validity of the indicators of falsity of the incisions on archaeological material, rendering them unfounded. Finally, an updated state of the falsehood of the inscriptions is presented, and directions for future research are proposed, emphasizing the need for a more rigorous and systematic approach to address the falsehood of incisions in ostraca.

Exploring nutritional myths and fake news: Impact and counteractions.

This article provides both a personal reflection concerning major professional and scientific challenges that can occur when evaluating the concept of hormesis and the historical foundations of cancer risk assessment/linear non-threshold (LNT) dose-response model and a detailed response to a recent critic. The assessment briefly captures what happened when an established mid-career scientist (i.e., the author) somehow went "astray" and challenged the central dose-response dogma of the scientific community "establishment" and regulatory agencies. It highlights what can and did happen to me when I was perceived to be a threat to vested interests; and how professional forces became animated, mobilized, and organized to marginalize me and my ideas/research to destroy my reputation and even get me removed from my position of having a tenured university full professorship. This historical background and personal story provide insights on their own but also necessary context when addressing new or recycled criticisms generated out of a mixture of legitimate scientific questions and/or ideological bias. There are also deep and vexing frustrations due to my substantial successes in the hormesis and cancer risk assessment areas. This framework provides a necessary backdrop to address recent criticisms of the Health Physics Society (HPS) documentary, The History of the Linear No-Threshold Model, and my publications on this topic that were the principal foundations for this documentary. In brief, Beyea's evaluation in the Health Physics Journal (HPJ) gives the impression that it is a broad evaluation of my research on the historical foundations of cancer risk assessment. Yet, Beyea addressed only a very limited set of discoveries that were discussed in the documentary. However, there have been numerous significant discoveries (about two dozen) published since the release of the documentary that he failed to acknowledge. All the new discoveries support, greatly enrich/extend, and do not contradict any aspect of the documentary. In addition, the Beyea assessment represents a recycling of information from his previous two highly compromised papers of nearly a decade ago. These papers, including the present one, display his confirmation bias that is strongly associated with his failure to use primary source materials, a source of novelty and significance in my historical research on cancer risk assessment. Failure to use primary sources greatly diminishes the historical and scientific value of the Beyea article, making it susceptible to secondary source opinion misinterpretations, frank errors, and bias, as repeatedly shown herein. In addition, Beyea attempts to damage my personal and professional standing/reputation, possibly violating ethical guidelines of the HPJ. Thus, the scientific basis for his comments is generally trivial and often devoid of historical foundation and accuracy, while failing to be representative of my body of work over the past two decades. The failure of Beyea to use a primary source-based research methodology, especially when such documents are copious, generally available, and essential for historical research on cancer risk assessment, represents a fundamental flaw that should have been addressed and corrected in the peer review process. Failure to do so led to the publication of a historically flawed and unreliable paper and calls into question the fairness of their review process.

The current research takes the mediating role of Good Public Governance (GPG) in the relationship between confirmation bias, oversight mechanisms and public sector financial reporting performance under forensic consideration. An explanatory quantitative study, where the primary data was obtained from 102 civil servants in charge of financial management and supervision working at the Muna regency government. Data were examined by SEM-PLS analysis. The results demonstrate that confirmation bias has a negative impact on good public governance, whereas internal control positively influences good public governance. On the other hand, there is no such effect for external monitoring. In addition, good public governance has significant positive effects on financial reporting performance and partially mediates confirmation bias, internal oversight (but not external oversight). Such results underline the significance of governance quality as an institutional channel that connects structural and behavioral factors with public sector performance, particularly in developing country local governments.

Purpose This study aims to investigate how memory shaped by selective exposure to politically controversial topics, specifically genetically modified organisms (GMO) food sourcing, affects consumers’ attitudes toward hospitality, particularly foodservice brands and their electronic word-of-mouth (e-WOM) intentions. Design/methodology/approach Study 1 evaluated participants’ existing attitudes toward GM foods and examined their confirmation bias through selective exposure. After completing the exposure tasks, the participant’s memory retention was measured. Study 2 investigated how the type of food sourcing message (man-made vs. all-natural) interacts with participants’ memory (pro-GMO vs. anti-GMO), affecting participants’ brand attitude and e-WOM. Findings In Study 1, participants with pro-GMO (vs. anti-GMO) attitudes showed better recall of pro-GMO (vs. anti-GMO) information, building upon confirmation bias. In Study 2, participants with a strong anti-GMO memory exhibited significantly more favorable brand attitudes and greater e-WOM intentions when exposed to messages emphasizing “all-natural” sourcing. Participants with stronger pro-GMO memory showed no significant response, regardless of the type of food sourcing. Research limitations/implications This study provides a theoretical basis for explaining brand message acceptance by incorporating selective exposure and memory into cognitive processing models. It also provides practical guidance for framing politically contentious messages so that communication can be adjusted for different segments of consumers. Originality/value This study indicates that consumers’ selective exposure and confirmation bias play an important role in shaping their reactions to politically sensitive brand messages in hospitality. Drawing on political psychology, this study reveals how biased memory and preexisting attitudes interact with GMO sourcing messages to shape brand attitudes and e-WOM intentions, offering implications for hospitality businesses managing their reputation in politically sensitive contexts.

ABSTRACT The construct of motivated reasoning has inspired an influential body of research. However, most theories of this construct are expressed in a verbal form. This is somewhat limited in light of contemporary research in cognitive science that emphasizes the insight afforded by employing computational modeling. To address this, the paper introduces a computational model of motivated reasoning. The model builds on previous accounts of belief formation based on Bayesian inference by adding computations concerning value or utility. The result is an interpretation of motivated reasoning as being akin to a process reflecting an unconscious Bayesian decision, in a way that is reminiscent of the famous Pascal’s wager. This framework is broadly consistent with empirical evidence, especially about the effect of loss function asymmetries on probability judgments, about the confirmation bias, and about the backfire effect. Moreover, it is compatible with evolutionary explanations of motivated reasoning that interpret this phenomenon as ensuing from self-deception. The model helps understanding the computational principles behind the concept of motivated reasoning. Moreover, it facilitates the comparison between perspectives that downplay motivated reasoning and theories that emphasize its role. This may inform empirical research aimed at establishing the real contribution of motivated reasoning during belief formation.

In the last decade, the proliferation of social media has transformed almost every aspect of human life—including financial decision-making. A new subset of online personalities known as financial influencers (“finfluencers”) has emerged across platforms like YouTube, Instagram, TikTok, Twitter, Reddit, and LinkedIn. These individuals post content related to investing, personal finance, market outlooks, stock tips, crypto strategies, and portfolio management, often reaching millions of users. Unlike traditional financial advisors, however, finfluencers usually operate outside formal regulatory frameworks, frequently without formal credentials, and monetize through sponsorships or affiliate marketing. This dynamic has profound implications for retail investors—especially novice and young investors—who increasingly rely on user-generated content as a primary source of financial information. This paper examines the nature and mechanisms through which social media and finfluencers influence investor behaviour. Drawing on both empirical studies and market data, the research explores motivators of investor reliance on social media, behavioural biases activated by digital content, impacts on investment choices and risk perception, and regulatory challenges that arise. It analyzes how social media content amplifies herd behaviour, confirmation bias, overconfidence, fear of missing out (FOMO), and risk misperception among investors. While finfluencers may democratize financial literacy and lower informational barriers, the quality of advice and credibility issues poses significant risks, ranging from misinformation to outright fraudulent schemes. Case studies and regional evidence, including research from India and Canada, reveal nuanced patterns across demographic groups. The paper concludes with recommendations for investors, educators, and regulators to mitigate harmful effects while preserving the potential educational benefits of digital finance content.